Federated learning, as a distributed learning that conducts the training on the local devices without accessing to the training data,
is vulnerable to Byzatine poisoning adversarial attacks. We argue that the federated learning model has to avoid those kind of
adversarial attacks through filtering out the adversarial clients by means of the federated aggregation operator. We propose a
dynamic federated aggregation operator that dynamically discards those adversarial clients and allows to prevent the corruption of
the global learning model. We assess it as a defense against adversarial attacks deploying a deep learning classification model in
a federated learning setting on the Fed-EMNIST Digits, Fashion MNIST and CIFAR-10 image datasets. The results show that the
dynamic selection of the clients to aggregate enhances the performance of the global learning model and discards the adversarial
and poor (with low quality models) clients.R&D&I grants - MCIN/AEI, Spain PID-2020-119478GB-I00
PID2020-116118GA-I00
EQC2018-005-084-PERDF A way of making EuropeMCIN/AEI FPU18/04475
IJC2018-036092-
